Spin transition in Gd3_3N@C80_{80}, detected by low-temperature on-chip SQUID technique

We present a magnetic study of the Gd$_3$N@C$_{80}$ molecule, consisting of a Gd-trimer via a Nitrogen atom, encapsulated in a C$_{80}$ cage. This molecular system can be an efficient contrast agent for Magnetic Resonance Imaging (MRI) applications. We used a low-temperature technique able to detect small magnetic signals by placing the sample in the vicinity of an on-chip SQUID. The technique implemented at NHMFL has the particularity to operate in high magnetic fields of up to 7 T. The Gd$_3$N@C$_{80}$ shows a paramagnetic behavior and we find a spin transition of the Gd$_3$N structure at 1.2 K. We perform quantum mechanical simulations, which indicate that one of the Gd ions changes from a $^8S_{7/2}$ state ($L=0, S=7/2$) to a $^7F_{6}$ state ($L=S=3, J=6$), likely due to a charge transfer between the C$_{80}$ cage and the ion

Cognitive Models as Simulators: The Case of Moral Decision-Making

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster. Here, we leverage this idea in the context of moral decision-making, by having reinforcement learning (RL) agents learn about fairness through interacting with a cognitive model of the Ultimatum Game (UG), a canonical task in behavioral and brain sciences for studying fairness. Interestingly, these RL agents learn to rationally adapt their behavior depending on the emotional state of their simulated UG responder. Our work suggests that using cognitive models as simulators of humans is an effective approach for training AI systems, presenting an important way for computational cognitive science to make contributions to AI

Cation occupancy determination in manganese zinc ferrites using Fourier transform infrared spectroscopy

The magnetic and electric properties of ferrites are influenced by the cation distribution within the crystalline spinel lattice. Methods such as extended x-ray-absorption fine structure(EXAFS) have been used to determine cation occupancies within the crystalline structure of materials such as manganesezincferrite (MZFO); however, it is not practical to be used for daily analysis. Fourier transform infrared (FTIR)spectroscopy is another technique which has the potential to determine cation occupancy while offering speed and convenience. In the literature it has been demonstrated that in ferrite systems FTIR data can be correlated to cation percentages when comparing tetrahedral (Td) and octahedral (Oh) sites. FTIRspectra were collected on a series of MZFO nanoparticles in the range from 200 to 600cm−1 and two absorbance peaks were observed. The first absorption region shifted with changing sample composition as calculated from transmission EXAFS experiments and elemental analysis. The data was normalized to the maximum of the peak of interest and the shifts were correlated to cation occupancy

Using Archival Data for I-O Research: Advantages, Pitfalls, Sources, and Examples

Two particular sets of experiences sparked our interest in writing this TIP article. The first was our increasing difficulty getting access to “new” organization- based samples. Depending on the topic and commitment involved, many organizations appear too leery and/or too strapped these days to allow for primary data collection. In addition, we have all experienced the disappointment of spending numerous hours on research proposals and meetings with organizational personnel, only to have the “plug pulled” at the last minute on a promising line of data collection. Conversely, we have also had experience with researchers in organizations who are willing and interested in partnering to analyze existing company data. A second experience that sparked our interest was supervising graduate student theses and dissertations. Students likely have even more difficulty than faculty in gaining access to organization-based samples. As a result, they often end up collecting survey data on “working students” or other campusbased convenience samples. Although we realize that “working students” may often be appropriate subjects, depending on the research questions being asked, it has been our experience that students often resort to this strategy even when it may not be appropriate, once they find they can’t obtain access to organization-based samples. Given these experiences, we thought a short TIP article outlining some of the key issues of using archival data for I-O research would be of interest to many TIP readers. We by no means foresee (or propose) the use of archival data sets becoming the principal “data collection strategy” within I-O psychology. Rather, we see this as an underutilized tool to be added to current and future I-O psychologists’ methodological toolbox. Given our extensive experiences working with a variety of sources of archival data, we realize there are numerous issues about which someone new to the area needs to be aware. Given the necessary brevity of a TIP article, we refer readers to key references cited throughout the rest of the paper for a detailed discussion of the issues raised below